Rock Mechanics
Seyed M. Fatemi Aghda; M. Kianpour; M. Talkhablou
Abstract
In this research, the relationship between P-wave velocity (Vp) and Electrical Resistivity (ER) parameters with rock mass quality indices is investigated; parameters such as rock mass quality classification (Q) and modified system for sedimentary rocks, known as Qsrm. For making predictive models, about ...
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In this research, the relationship between P-wave velocity (Vp) and Electrical Resistivity (ER) parameters with rock mass quality indices is investigated; parameters such as rock mass quality classification (Q) and modified system for sedimentary rocks, known as Qsrm. For making predictive models, about 1200 data-sets extracted from sections drilled in Seymareh and Karun 2 Dam Sites (SDS and KDS) in Asmari Formation, south-west Iran. Statistical and fuzzy methods used to study the relationships between physical characteristics and rock mass quality. Since in Qsrm classification, the existence of cavities, layering and rock texture is considered in addition to the parameters considered in the Q classification; therefore, it provides a better description of rock mass and is closely related with Vp and ER parameters. The obtained equations for predicting Q and Qsrm showed the determination coefficients (R2) 0.48 and 0.67, respectively, and the coefficient of determination 0.86 for Qsrm calculated from the fuzzy model. Finally, Mean Absolute Deviation (MAD), Variance Accounted For (VAF) and Root Mean Square Error (RMSE) used to check the prediction performance of statistical and fuzzy methods. The results of the calculated errors also showed that fuzzy models are interesting because they have good accuracy for predicting Qsrm. In addition, by increasing the degree of karstifiction, the efficiency of the geophysical method for estimate of Q decreases rapidly, this is due to ignoring the cavities in these categories.
Exploitation
S. Barak; A. Bahroudi; G. Jozanikohan
Abstract
The purpose of mineral exploration is to find ore deposits. The main aim of this work is to use the fuzzy inference system to integrate the exploration layers including the geological, remote sensing, geochemical, and magnetic data. The studied area was the porphyry copper deposit of the Kahang area ...
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The purpose of mineral exploration is to find ore deposits. The main aim of this work is to use the fuzzy inference system to integrate the exploration layers including the geological, remote sensing, geochemical, and magnetic data. The studied area was the porphyry copper deposit of the Kahang area in the preliminary stage of exploration. Overlaying of rock units and tectonic layers were used to prepare the geological layer. ASTER images were used for the purpose of recognition of the alterations. The processes used for preparation of the alteration layer were the image-based methods including RGB, band ratio, and principal component analysis as well as the spectrum-based methods including spectral angel mapper and spectral feature fitting. In order to prepare the geochemical layer, the multivariate statistical methods such as the Pearson correlation matrix and cluster analysis were applied on the data, which showed that both copper and molybdenum were the most effective elements of mineralization. Application of the concentration-number multi-fractal modeling was used for geochemical anomaly separation, and finally, the geochemical layer was obtained by the overlaying of two prepared layers of copper and molybdenum. In order to prepare the magnetics layer, the analytical signal map of the magnetometry data was selected. Finally, the FIS integration was applied on the layers. Ultimately, the mineral potential map was obtained and compared with the 33 drilled boreholes in the studied area. The accuracy of the model was validated upon achieving the 70.6% agreement percentage between the model results and true data from the boreholes, and consequently, the appropriate areas were suggested for the subsequent drilling.
M. Doustmohammadi; A. Jafari; O. Asghari
Abstract
Water inflow is one of the most important challenges in the underground excavations. In addition to inducing working conditions and environmental problems, it decreases the stability and quality of the surrounding rocks. The direct method of measuring rock mass hydraulic conductivity consists of drilling ...
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Water inflow is one of the most important challenges in the underground excavations. In addition to inducing working conditions and environmental problems, it decreases the stability and quality of the surrounding rocks. The direct method of measuring rock mass hydraulic conductivity consists of drilling the boreholes and observing the rate of fluid lost in the boreholes. Applying this method is still problematic due to the depth of underground spaces, and also the groundwater level covering them. Therefore, many researchers have tried to predict the water inflow indirectly. This paper attempts to predict the groundwater conditions in the Beheshtabad tunnel (in Iran) using the fuzzy inference system based on the datasets acquired from the preliminary exploration studies. 250 datasets for the Beheshtabad tunnel were used out of which, 200 datasets were used to develop the model and 50 were used to validate the results obtained. 90% accuracy was obtained through comparing the fuzzy estimation and actual groundwater conditions. The proposed model can be used with much less degree of complexity for prediction of the groundwater conditions as well as decreasing the overall costs of the exploration measurements, and due to these characteristics, it is applicable for most users.